Keynote & Invited Speakers

Keynote Speakers

Keynote Speaker I

Prof. Jie Yang, Shanghai Jiao Tong University, China

Bio: Jie Yang received a bachelor’s degree in Automatic Control in Shanghai Jiao Tong University (SJTU), where a master’s degree in Pattern Recognition & Intelligent System was achieved three years later. In 1994, he received Ph.D. at Department of Computer Science, University of Hamburg, Germany. Now he is the Professor and Director of Institute of Image Processing and Pattern recognition in Shanghai Jiao Tong University. He is the principal investigator of more than 30 national and ministry scientific research projects in image processing, pattern recognition, data mining, and artificial intelligence. He has published six books，more than five hundreds of articles in national or international academic journals and conferences. Google citation 24964，H-index 81. Up to now, he has supervised 6 postdoctoral, 48 doctors and 70 masters, awarded six research achievement prizes from ministry of Education, China and Shanghai municipality. He has owned 48 patents. Three Ph.D. dissertation he supervised was evaluated as “National Best Ph.D. Dissertation” in 2009, in 2017, in 2019. He has been chairman and keynote speaker of more than 10 international conferences.

Keynote Speaker II

Prof. Hiroshi Fujita, Gifu University, Japan

Bio: Prof. Hiroshi Fujita received Ph.D. degree from Nagoya University in 1983. He was a visiting researcher at the K.Rossmann Radiologic Image Laboratory, University of Chicago, in 1983-1986. He became an associate professor in 1991 and a professor in 1995 in the Faculty of Engineering, Gifu University. He has been a professor and chair of intelligent image information since 2002 at the Graduate School of Medicine, Gifu University. He is now a Research Professor of Gifu University. He is a member of the Society for Medical Image Information (Honorary President), the Institute of Electronics, Information and Communication Engineers (IEICE, Fellow), and the Japan Society for Medical Imaging Technology (Honorary member). He has served as chairman and keynote speaker at many scientific conferences both domestically and internationally. His research interests include computer-aided diagnosis (CAD) system, medical AI, image analysis/processing/evaluation in medicine. He received numerous awards such as the Medical Imaging Information Society Award (2018), RSNA (2001, 6 others), SPIE (1995, 8 others), etc. He has co-authored over 1,000 articles in scientific journals, international conference proceedings, book chapters, and scientific magazines. In 2024, he was recognized in both the “Single-Year” and “Lifetime” categories of the “Top 2% of Scientists in the World” ranking, published by Stanford University and Elsevier.

Invited Speakers

Invited Speaker I

Associate Professor Nian Wang, Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA

Bio: Dr. Nian Wang is an Associate Professor in the Advanced Imaging Research Center at UT Southwestern Medical Center. My expertise is in developing novel quantitative MRI techniques and analysis methods on the CNS system and musculoskeletal system. His lab has extended the research on the technical development and methodology innovation in medical imaging and Artificial Intelligence (AI) and their translation to MRI, x-ray, and optical imaging. He has published more than 70 papers in reputed journals and been invited to devliver talks in many international conferences.

Speech Title: High-resolution MRI Predicted Whole Mouse Brain Cell Type Atlas using Multi-modal Fusion Network (MFNet)

Speech Abstract: Mapping brain cell types with high spatial precision is fundamental for understanding brain structure and function. Traditional cell atlases, such as the Allen Brain Cell Atlas, rely on labor-intensive techniques like single-cell sequencing and spatial transcriptomics, often at limited spatial resolution. In this study, we present a deep learning framework—Multi-modal Fusion Network (MFNet)—that predicts neuronal cell types across the whole mouse brain by integrating high-resolution diffusion MRI (dMRI) and multi-modal datasets from the BICCN mouse brain atlas. Using the Allen Brain Cell Atlas as ground truth, we trained MFNet to predict dominant neuronal cell neighborhoods and classes, taking advantage of regional specificity and hierarchical cell-type structures. High-resolution (45 µm isotropic) ex vivo dMRI was acquired and processed with multiple diffusion models (DTI, DKI, NODDI, SANDI), then registered to the Allen Mouse Brain Common Coordinate Framework (CCFv3), along with spatial transcriptomics and epigenomics data. MFNet produces a full-scale cell type atlas at 10 µm isotropic resolution, significantly enhancing the spatial detail of the original atlas. Additionally, we examined correlations between dMRI features and gene expression of marker genes. This study offers a novel, efficient method for generating high-resolution brain cell atlases and demonstrates the potential of advanced imaging combined with deep learning to decode brain cellular architecture and its relevance to development and disease.

Invited Speaker II

Associate Professor Yanlei Gu,  Hiroshima University, Japan

Bio: Dr. Yanlei Gu received his Ph.D. from Nagoya University. He subsequently worked as a researcher at the University of Tokyo and was awarded a Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship for Overseas Researchers. In 2019, he joined Ritsumeikan University as a Lecturer, and since 2024, he has been an Associate Professor at Hiroshima University. He also serves as a Visiting Researcher at the University of Tokyo.
His research interests include Autonomous Driving, Intelligent Transportation Systems (ITS), Machine Learning, and Computer Vision. He has published more than 120 peer-reviewed journal and international conference papers. His research has received several awards, including Best Paper Awards and other honors from IEEE and international conferences.
He is a member of the IEEE Intelligent Transportation Systems Society and the IEEE Consumer Technology Society. He has served on the organizing committees of IEEE ICVES 2015 and IEEE ITSC 2017, as well as on the technical program committees of ICIPRob 2021, 2022, 2024, and 2026.